1. Technical Field
The present invention relates to characteristic-based profiling systems and, more particularly, to combining multiple points of data regarding individuals through the use of characteristics in order to determine the relationship between the individuals and a user-defined criteria.
2. Description of the Related Art
Customer profiling systems are known in the art. Traditional systems include consumer rewards cards, credit card purchase information, demographic profiling, behavioral profiling, and customer surveying. Some businesses supplement these traditional systems with website and social media analytic tools that profile the business's fans and followers according to factors such as “likes,” “click-through rates,” and search engine queries, among others. Generally, these systems attempt to determine products, promotions, and advertisements that are most likely to appeal to a specific customer or broad customer segment. This information helps businesses forecast future market behavior, manage their product portfolio and inventory levels, adjust product pricing, design marketing strategies, and determine human resource and capital investment needs in order to increase revenue, market share, and profitability. For example, advertising targeted at customers who are most likely to purchase a product may be more effective than advertising targeting broader audiences. Likewise, products that are related to one another are likely to be purchased by the same customer and may sell better if offered at the same time, whether as a package or as separate items. Online retailers often use a similar approach, suggesting items that other customers frequently purchase in conjunction with the selected item.
While most approaches create basic customer profiles, these profiles do not reflect the myriad similarities between customers or the numerous ways in which customers can be grouped. For example, most approaches generally provide profiles on either an individual customer or an overly broad customer segment (for example, all women ages 25-34 with a college degree), failing to reflect the various degrees of granularity with which customers can be grouped. One type of approach typically uses only historical, static, and quantitative or objective information. As a result, customer profiles created by these approaches are generally outdated and inaccurate, and fail to account for the vast amount of potentially rich, but qualitative and subjective, information about the customer that is available to most businesses. A second type of approach uses only subjective or qualitative information. These approaches also have drawbacks. Typically they use expensive and time-consuming methods such as customer surveys or focus groups. Due to the nature of the setting, the results may not accurately reflect the attitudes or opinions of the surveyed individuals. Due to the expense and time involved, only a limited number of individuals may be surveyed. Likewise, the purchasing suggestions created by these systems are often inaccurate. For example, while many customers who purchase item A also purchase item B, that information does not provide any insight into what a specific customer, or group of customers, may be interested in.
Additionally, customer information is often collected with respect to a single business metric and may never be used to glean insights about other metrics that may be helpful to the company. This is particularly true for businesses that are growing and those that have multiple departments. Growing businesses must usually adjust or supplement its performance metrics to reflect new goals, strategies, and business operations. As a result, these businesses must understand how its customers relate to the new set of business metrics rather than, or in addition to, the ones for which the data was originally collected. Similarly, businesses with multiple departments frequently gather customer information for purposes of a department-specific metric, but fail to use that information across other departments or globally within the organization. For example, a business may have a marketing department and risk management department. Customer information gathered by the marketing department when researching new product markets may never be seen or used by the risk management team to determine whether that customer or market poses undue risk to the business. Methods for combining this disparate data, (for example, a technique sometimes referred to as “one version of the truth analysis”) do not allow the business to apply the same method to external data it may be interested in. Furthermore, these systems are used only to organize the information and are not useful for analyzing it.
For example, companies may gather information to attempt to measure the sentiment that individuals, or groups of individuals, express towards certain topics (for example, a specific product, brand, company, issue, or person). This may allow an organization to determine how people perceive it, or topics important to it.
Sentiment analysis methods are capable of identifying the sentiment expressed in a single statement, or the overall sentiment expressed by multiple statements; however, these methods typically cannot determine whether this sentiment accurately reflects the individual's (or group's) true feelings, or simply reflects the mannerisms and style of that individual or group. For example, a certain individual may generally express strong sentiments as part of their personal style, even when they do not feel strongly about the topic they are discussing. Sentiment analysis methods may incorrectly determine that this individual has a strong sentiment towards a given topic, based on the sentiment normally expressed by that individual. Thus, there is a need for a more accurate sentiment analysis method that takes into account the baseline sentiment expressed by each individual or group, to more accurately determine that individual's or group's sentiment towards a given topic.
In addition, sentiment analysis methods are unable to determine which individuals matter the most to an organization. For example, a business may wish to identify which customers have the most impact on a marketing campaign. Likewise, an organization may wish to identify individuals that have absolutely no significance to it whatsoever, and should therefore be ignored. These sentiment analysis methods may be able to identify which individuals have positive or negative sentiment about the campaign. However, these methods will not be able to determine which of those individuals are also the most important to that business. For example, an organization may wish to identify individuals who make statements that influence others. A positive or negative review by one individual (for example, a technology critic) may be taken more seriously, and influence more people, than a similar review by another individual. In other cases, the sentiment expressed by an individual may be a reliable predictor of larger trends, even if that individual's statement is not influential. For example, certain individuals may have insights that are later expressed by large groups, even if the group is unaware of that insight. Thus, an organization may wish to identify these insightful individuals, in order to determine trends and attitudes as early as possible.
Likewise, an organization may wish to know how a positive or negative sentiment expressed by an individual corresponds to certain behavior by that same individual. For example, whether an individual who makes a negative statement about a service later discontinues that service. This may allow the organization to determine which dissatisfied customers it should offer the largest incentives to continue their service. A business might also wish to know which individuals that make positive statements about a product later purchase that product. This may allow a business to identify which potential customers are most likely to make a purchase, in order to target those customers with incentives. A business may also want to identify people with similar sentimental patterns and characteristic based profiles so as to better understand how to address, prepare for, and anticipate various situations, changes in situations, relationships, customer feelings, reactions, or sentiments.
The organization may also wish to track the change in an individual's sentiment based on certain actions by the organization. For example, whether an incentive was effective in retaining a customer because it actually changed the customer's sentiment, or whether the customer has retained service because of the incentive, but remains dissatisfied. Or, whether a certain marketing campaign affects individuals' sentiment towards a product.
As a result, there is a need for a system that addresses the issues above.